Yan Tianying, Duan Long, Chen Xiaopan, Gao Pan, Xu Wei
College of Information Science and Technology, Shihezi University Shihezi 832003 China
Key Laboratory of Oasis Ecology Agriculture, Shihezi University Shihezi 832003 China.
RSC Adv. 2020 Nov 18;10(68):41936-41945. doi: 10.1039/d0ra06925f. eCollection 2020 Nov 11.
is used as a functional food and traditional medicine. The geographical origin of is a determinant factor influencing the chemical and physical properties as well as its medicinal and health effects. The visible/near-infrared (Vis/NIR) (376-1044 nm) and near-infrared (NIR) hyperspectral imaging (915-1699 nm) were used to identify the geographical origin of . Convolutional neural network (CNN) and recurrent neural network (RNN) models in deep learning methods were built using extracted spectra, with logistic regression (LR) and support vector machine (SVM) models as comparisons. For both spectral ranges, the deep learning methods, LR and SVM all exhibited good results. The classification accuracy was over 90% for the calibration, validation, and prediction sets by the LR, CNN, and RNN models. Slight differences in classification performances existed between the two spectral ranges. Further, interpretation of the CNN model was conducted to identify the important wavelengths, and the wavelengths with high contribution rates that affected the discriminant analysis were consistent with the spectral differences. Thus, the overall results illustrate that hyperspectral imaging with deep learning methods can be used to identify the geographical origin of , which provides a new basis for related research.
被用作功能性食品和传统药物。其地理来源是影响其化学和物理性质以及药用和健康效果的一个决定性因素。可见/近红外（Vis/NIR）（376 - 1044纳米）和近红外（NIR）高光谱成像（915 - 1699纳米）被用于识别其地理来源。利用提取的光谱构建了深度学习方法中的卷积神经网络（CNN）和循环神经网络（RNN）模型，并以逻辑回归（LR）和支持向量机（SVM）模型作为比较。对于这两个光谱范围，深度学习方法、LR和SVM均表现出良好的结果。LR、CNN和RNN模型在校准集、验证集和预测集上的分类准确率均超过90%。两个光谱范围之间的分类性能存在细微差异。此外，对CNN模型进行了解释以识别重要波长，影响判别分析的高贡献率波长与光谱差异一致。因此，总体结果表明，结合深度学习方法的高光谱成像可用于识别其地理来源，这为相关研究提供了新的依据。
